JuliaLibWrapping

JuliaLibWrapping generates C headers and Python ctypes bindings for shared libraries compiled from Julia by juliac. It turns the ABI metadata that juliac emits into wrappers that let non-Julia programs call the compiled library as if it were any other native dependency.

New to the package? Start with the tutorial — it walks a small library end to end, from Julia source through pip install to numpy-flavored Python.

Who it is for

Authors of Julia libraries who want to ship compiled code that downstream users — currently C or Python programmers — can use without installing a Julia runtime themselves. The compiled library is produced by juliac; this package produces the binding code that makes it usable from the target language.

The two-tool split

The pipeline is split deliberately across two packages:

juliac / JuliaC.jl --emits--> JSON ABI-info file --consumed by--> JuliaLibWrapping --emits--> .h / Python package

JuliaC.jl compiles the shared library and emits a JSON file describing its ABI, but does not generate wrappers. JuliaLibWrapping consumes that JSON and emits the wrappers. Coupling between the two repos is the JSON format alone.

build_library runs both halves in one call when JuliaC.jl is loaded; see Concepts for the pipeline architecture and the bundling, multi-library, and two-tier output stories.

Where to go next

• Tutorial: wrap an OLS regression library: build a small library with a Python wrapper using numpy.
• Concepts: the pipeline, the ABI data model, the extension point for new target languages, and the runtime-closure / bundling story.
• JLWInterop: a small package needed by almost any wrapped Julia module. Defines a few interoperability types (CArray, CString, and JLWStatus) that your Julia wrapper-code should use to ensure interopability with the language binding.
• Error handling across the ABI: the JLWStatus convention that lets wrapped libraries surface errors as native exceptions in the target language.
• API reference: the public API, in full detail.